Rapid Threat Detection Research Articles

Revolutionizing Cybersecurity with AI: Predictive Threat Intelligence and Automated Response Systems

The sophistication and breadth of cyber threats are continuously expanding, making it more difficult for traditional security measures to keep up. Artificial intelligence is revolutionizing cybersecurity by equipping businesses to proactively counter threats with automated reaction systems and predictive threat intelligence. Data analytics, behavioral analysis, and machine learning enable AI-powered systems to anticipate cyber assaults, enabling more efficient and rapid threat detection. By automating reaction mechanisms and mitigating threats in real-time, AI systems can minimize human error and maximize damage mitigation. AI techniques, such as anomaly detection, predictive modeling, and real-time threat analysis; data privacy, ethics, and the risks of hostile attacks are among the subjects covered, as are the benefits and drawbacks of utilizing AI in cybersecurity. This article provides the framework for future intelligent, automated cyber defense methods and illustrates how AI may alter cybersecurity using real-life examples and case studies.

Optimization of machine learning model training procedure on multi-gpu systems to enhance cyber security in telecommunication networks

This paper analyzes the optimization features of machine learning (ML) model training procedures using multi-GPU systems to enhance cyber security in telecommunication networks. A key aspect of the study is the use of data parallelism, which allows the distribution of the training load across multiple GPUs, significantly reducing training time and improving model accuracy-critical factors for rapid threat detection in cyberspace. A novel approach for optimizing data batch size using Mutual Information (MI) is proposed, which harmonizes the utilization of computational resources with the information content of the training data. MI helps to determine the optimal data batch size that minimizes training errors and improves model accuracy without a significant increase in training time. Experimental results demonstrate the substantial advantages of multi-GPU configurations compared to single-GPU setups, providing faster training and improved model accuracy. It was particularly emphasized that MI-guided batch size tuning significantly outperforms traditional manual tuning methods, ensuring higher validation accuracy and reducing training time. The study showed that the MI-based approach is an effective tool for addressing the problem of optimizing ML model training processes in real-world scenarios where cyber security is critical. The proposed methods allow ML models to train faster and more accurately identify potential threats, making them particularly relevant for telecommunication networks where a rapid response to new threats in real time is required. The implementation of modern computational technologies such as multi-GPU systems and MI-optimized training enhances the efficiency and accuracy of machine learning models. This, in turn, improves cyber security measures and ensures a more reliable defence of telecommunication networks against malicious attacks. It is noted that the proposed approaches can be adapted not only for cyber security but also for other areas where high model accuracy and fast training are important. Future research prospects include the development of new machine learning methods, particularly deep neural networks, the exploration of alternative computational architectures such as quantum computing or distributed systems, and their integration into real-time systems. Special attention should be paid to the ethical aspects of implementing automated cyber security systems, particularly in preventing bias in algorithms and ensuring fairness in their application.

Determining the trend of geometrical changes of a hydrotechnical object based on data in the form of LiDAR point clouds

Monitoring the technical condition of hydrotechnical facilities is crucial for ensuring their safe usage. This process typically involves tracking environmental variables (e.g., concrete damming levels, temperatures, piezometer readings) as well as geometric and physical variables (deformation, cracking, filtration, pore pressure, etc.), whose long-term trends provide valuable information for facility managers. Research on the methods of analyzing geodetic monitoring data (manual and automatic) and sensor data is vital for assessing the technical condition and safety of facilities, particularly when utilizing new measurement technologies. Emerging technologies for obtaining data on the changes in the surface of objects employ laser scanning techniques (such as LiDAR, Light Detection, and Ranging) from various heights: terrestrial, unmanned aerial vehicles (UAVs, drones), and satellites using sensors that record geospatial and multispectral data. This article introduces an algorithm to determine geometric change trends using terrestrial laser scanning data for both concrete and earth surfaces. In the consecutive steps of the algorithm, normal vectors were utilized to analyze changes, calculate local surface deflection angles, and determine object alterations. These normal vectors were derived by fitting local planes to the point cloud using the least squares method. In most applications, surface strain and deformation analyses based on laser scanning point clouds primarily involve direct comparisons using the Cloud to Cloud (C2C) method, resulting in complex, difficult-to-interpret deformation maps. In contrast, preliminary trend analysis using local normal vectors allows for rapid threat detection. This approach significantly reduces calculations, with detailed point cloud interpretation commencing only after detecting a change on the object indicated by normal vectors in the form of an increasing deflection trend. Referred to as the cluster algorithm by the authors of this paper, this method can be applied to monitor both concrete and earth objects, with examples of analyses for different object types presented in the article.

Low and high spatial frequencies contribute equally to rapid threat detection when contrast is normalized

Low and high spatial frequencies contribute equally to rapid threat detection when contrast is normalized

DIVERGENCE: Deep Reinforcement Learning-Based Adaptive Traffic Inspection and Moving Target Defense Countermeasure Framework

Reinforcement learning (RL) is a promising approach for intelligent agents to protect a given system under highly hostile environments. RL allows the agent to adaptively make sequential defense decisions based on the perceived current state of system security aiming to achieve the maximum defense performance in terms of fast, efficient, and automated detection, threat analysis, and response to the threat. In this paper, we propose a deep reinforcement learning (DRL)-based adaptive traffic inspection and moving target defense countermeasure framework, called ‘DIVERGENCE,’ for building a secure networked system. The DIVERGENCE provides two main security services: (1) a DRL-based network traffic inspection mechanism to achieve scalable and intensive network traffic visibility for rapid threat detection; and (2) an address shuffling-based moving target defense (MTD) technique to defend against threats as a proactive intrusion prevention mechanism. Through extensive simulations and experiments, we demonstrate that the DIVERGENCE successfully caught malicious traffic flows while significantly reducing the vulnerability of the network through MTD.

The importance of using multiple outcome measures in infant research.

Collecting data with infants is notoriously difficult. As a result, many of our studies consist of small samples, with only a single measure, in a single age group, at a single time point. With renewed calls for greater academic rigor in data collection practices, using multiple outcome measures in infant research is one way to increase rigor, and, at the same time, enable us to more accurately interpret our data. Here, we illustrate the importance of using multiple measures in psychological research with examples from our own work on rapid threat detection and from the broader infancy literature. First, we describe our initial studies using a single outcome measure, and how this strategy caused us to nearly miss a rich and complex story about attention biases for threat and their development. We demonstrate how using converging measures can help researchers make inferences about infant behavior, and how using additional measures allows us to more deeply examine the mechanisms that drive developmental change. Finally, we provide practical and statistical recommendations for how researchers can use multiple measures in future work.

Prioritized attentional processing: Acute stress, memory and stimulus emotionality facilitate attentional disengagement

Rapid attentional orienting toward relevant stimuli and efficient disengagement from irrelevant stimuli are critical for survival. Here, we examined the roles of memory processes, emotional arousal and acute stress in attentional disengagement. To this end, 64 healthy participants encoded negative and neutral facial expressions and, after being exposed to a stress or control manipulation, performed an attention task in which they had to disengage from these previously encoded as well as novel face stimuli. During the attention task, electroencephalography (EEG) and pupillometry data were recorded. Our results showed overall faster reaction times after acute stress and when participants had to disengage from emotionally negative or old facial expressions. Further, pupil dilations were larger in response to neutral faces. During disengagement, our EEG data revealed a reduced N2pc amplitude when participants disengaged from neutral compared to negative facial expressions when these were not presented before, as well as earlier onset latencies for the N400f (for disengagement from negative and old faces), the N2pc, and the LPP (for disengagement from negative faces). In addition, early visual processing of negative faces, as reflected in the P1 amplitude, was enhanced specifically in stressed participants. Our findings indicate that attentional disengagement is improved for negative and familiar stimuli and that stress facilitates not only attentional disengagement but also emotional processing in general. Together, these processes may represent important mechanisms enabling efficient performance and rapid threat detection.

Unconscious processing of coarse visual information during anticipatory threat.

Rapid detection of threats has been proposed to rely on automatic processing of their coarse visual features. However, it remains unclear whether such a mechanism is restricted to detection of threat cues, or whether it reflects a broader sensitivity to even neutral coarse visual information features during states of threat. We used a backward masking task in which participants discriminated the orientation of subliminally presented low (3 cpd) and high (6 cpd) spatial frequency gratings, under threat (of shock) and safe conditions. Visual awareness of the gratings was assessed objectively using an additional localization task. When participants were unaware of the gratings, above chance and improved discrimination of low-spatial frequency gratings was observed under threat compared to safe trials. These findings demonstrate unconscious processing of neutral coarse visual information during threat state, supporting the view that automatic threat detection may rely on a general facilitation of coarse features irrespective of threat content.

Oxytocin facilitates adaptive fear and attenuates anxiety responses in animal models and human studies-potential interaction with the corticotropin-releasing factor (CRF) system in the bed nucleus of the stria terminalis (BNST).

Despite its relatively well-understood role as a reproductive and pro-social peptide, oxytocin (OT) tells a more convoluted story in terms of its modulation of fear and anxiety. This nuanced story has been obscured by a great deal of research into the therapeutic applications of exogenous OT, driving more than 400 ongoing clinical trials. Drawing from animal models and human studies, we review the complex evidence concerning OT's role in fear learning and anxiety, clarifying the existing confusion about modulation of fear versus anxiety. We discuss animal models and human studies demonstrating the prevailing role of OT in strengthening fear memory to a discrete signal or cue, which allows accurate and rapid threat detection that facilitates survival. We also review ostensibly contrasting behavioral studies that nonetheless provide compelling evidence of OT attenuating sustained contextual fear and anxiety-like behavior, arguing that these OT effects on the modulation of fear vs. anxiety are not mutually exclusive. To disambiguate how endogenous OT modulates fear and anxiety, an understudied area compared to exogenous OT, we survey behavioral studies utilizing OT receptor (OTR) antagonists. Based on emerging evidence about the role of OTR in rat dorsolateral bed nucleus of stria terminalis (BNST) and elsewhere, we postulate that OT plays a critical role in facilitating accurate discrimination between stimuli representing threat and safety. Supported by human studies, we demonstrate that OT uniquely facilitates adaptive fear but reduces maladaptive anxiety. Last, we explore the limited literature on endogenous OT and its interaction with corticotropin-releasing factor (CRF) with a special emphasis on the dorsolateral BNST, which may hold the key to the neurobiology of phasic fear and sustained anxiety.

Deconstructing the snake: The relative roles of perception, cognition, and emotion on threat detection.

Previous research has documented a propensity for rapid detection of various threats like snakes and spiders in human adults, children, and even infants. The current research presents a controlled, systematic investigation of the mechanisms by which humans quickly detect threat. In 3 experiments, we examine the unique and interacting roles of low-level perceptual cues, cognitive factors such as threatening labels, and emotional state to rapid threat detection. Across studies, low-level perceptual features of snakes-namely, curvilinear shapes-consistently elicited rapid detection. Using threatening labels (Experiment 2) facilitated detection marginally more, and a fearful emotional induction (Experiment 3) facilitated detection even further. Collectively the results offer a more complete picture of the mechanisms by which humans quickly perceive threat, suggesting that rapid threat detection can result from several individual and interacting factors, including perceptual, cognitive, and emotional.

Emotional Bias in Childhood-Event Interpretation by Adults with Generalized Anxiety Disorder

Objectives: It is often stressed that anxiety alters perceptions of the world by way of processes like the rapid detection of threats and the exaggeration of risks. Given that these processes are active during all information processing, they are thought to influence not only the interpretation of current events, but also the recall of past events. In patient’s anamnesis, we have often attempted to incorporate the events that patients recollect into our functional explanation of their disorder, forgetting that the meaning of what a patient relates may be intrinsically linked to his/her pathology and pathological functioning. We are interested here in potential biases in the events remembered by anxious patients. Methods: We contacted twenty-seven men aged 25 or older suffering from generalized anxiety disorder. They were asked to recollect childhood events from cue words and then rate each remembered event on its subjective emotional valence and intensity, its frequency of recollection, and the vividness of the memory. The responses of the anxious subjects were compared to those of control subjects without psychiatric disorders. Results: Findings seem to show that anxious patients’ memories of childhood events may be impaired by emotional interpretation biases. Indeed, anxious patients remember more negative events than participants in the control group. The emotional intensity of negative or positive events remembered by anxious patients is also considered more important.

Neuropsychological aspects of delusional disorder

Delusional disorders (DDs) are clinically rare syndromes characterized by false beliefs that are held with firm conviction despite counterevidence. The neuropsychology of DDs is poorly understood. Two partially opposing models--a cognitive bias model and a cognitive deficit model--have received mixed empiric support, partly because most research has been carried out in patients with paranoid schizophrenia, with which the nosologic association of DDs is unknown. Based on these models, we review empiric findings concerning the neuropsychology of DDs (narrowly defined). We conclude that DDs can best be seen as extreme variations of cognitive mechanisms involved in rapid threat detection and defensive harm avoidance. From this viewpoint, the two models seem to be complementary in explanatory power rather than contradictory. Future research may help to clarify the question of gene-environment interaction involvement in the formation of delusional beliefs.

Fear Is Fast in Phobic Individuals: Amygdala Activation in Response to Fear-Relevant Stimuli

Two core characteristics of pathologic fear are its rapid onset and resistance to cognitive regulation. We hypothesized that activation of the amygdala early in the presentation of fear-relevant visual stimuli would distinguish phobics from nonphobics. Chronometry of amygdala activation to phobia-relevant pictures was assessed in 13 spider phobics and 14 nonphobics using functional magnetic resonance imaging (fMRI). Blood oxygen level-dependent (BOLD) responses in the amygdala early in picture processing consistently differentiated between phobic and nonphobic subjects, as well as between phobogenic and nonphobogenic stimuli among phobics. Furthermore, amygdalar BOLD responses associated with timing but not magnitude of activation predicted affective responses to phobogenic stimuli. Computational modeling procedures were used to identify patterns of neural activation in the amygdala that could yield the observed BOLD data. These data suggest that phobic responses were characterized by strong but brief amygdala responses, whereas nonphobic responses were weaker and more sustained. Results are discussed in the context of the amygdala's role in rapid threat detection and the vigilance-avoidance hypothesis of anxiety. These data highlight the importance of examining the neural substrates of the immediate impact of phobogenic stimuli for understanding pathological fear.